Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarci...Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.展开更多
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit...Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.展开更多
The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have eme...The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.展开更多
目的探讨项目式学习在护理实习生岗前培训中对锐器伤防护技能培训的效果及其对实习期间锐器伤发生率的影响。方法采用便利抽样法选取2023年3月南昌理工学院医学院护理专业A、B两个班级为研究对象。采用掷硬币法将A班设为常规组,B班设为...目的探讨项目式学习在护理实习生岗前培训中对锐器伤防护技能培训的效果及其对实习期间锐器伤发生率的影响。方法采用便利抽样法选取2023年3月南昌理工学院医学院护理专业A、B两个班级为研究对象。采用掷硬币法将A班设为常规组,B班设为干预组。常规组接受以回顾性强化训练护理操作技能为主的常规岗前培训;干预组在此基础上融入锐器伤防护技能的项目式学习补偿教育。采用柯氏四级培训评估模式在相应阶段自“反应、学习、行为、结果”四个递进的层面全方位评估教育效果。结果A班常规组和B班干预组各纳入56名护理实习生。干预组护理实习生的课程评价评分(128.67±4.39 VS 117.28±6.55)、针刺伤防护知识认知评分(109.11±4.38 VS 96.44±6.72)、安全注射行为评分(38.45±4.91 VS 32.30±5.62)、职业认同感评分(58.02±8.55 VS 51.77±15.86)、岗位胜任力评分(82.59±13.35 VS 75.61±15.09)均高于常规组,差异均有统计学意义(均P<0.05)。干预组护理实习生锐器伤发生率(19.64%VS 57.14%)及平均发生频次(1.45 VS 2.13)均低于常规组;锐器伤后例次干预率(87.50%VS 45.59%)和例次上报率(93.75%VS 32.35%)均高于常规组,差异均有统计学意义(均P<0.05)。结论在护生岗前培训中引入项目式学习的锐器伤防护培训,能有效提升防护技能掌握程度,降低实习期间锐器伤发生率,对培养护生职业防护能力具有重要实践价值。展开更多
基金Project(2301DH09002)supported by the Bureau of Planning and Natural Resources,Chongqing,ChinaProject(2022T3051)supported by the Science and Technology Service Network Initiative,ChinaProject(2018-ZL-01)supported by the Sichuan Transportation Science and Technology,China。
文摘Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.
基金supported by the National Natural Science Foundation of China(6177340561751312)the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。
文摘Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
基金financial support received from DST-SERBSRG/2020/000997,Indiathe initiation grant received from IIT Kanpur。
文摘The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.
文摘目的探讨项目式学习在护理实习生岗前培训中对锐器伤防护技能培训的效果及其对实习期间锐器伤发生率的影响。方法采用便利抽样法选取2023年3月南昌理工学院医学院护理专业A、B两个班级为研究对象。采用掷硬币法将A班设为常规组,B班设为干预组。常规组接受以回顾性强化训练护理操作技能为主的常规岗前培训;干预组在此基础上融入锐器伤防护技能的项目式学习补偿教育。采用柯氏四级培训评估模式在相应阶段自“反应、学习、行为、结果”四个递进的层面全方位评估教育效果。结果A班常规组和B班干预组各纳入56名护理实习生。干预组护理实习生的课程评价评分(128.67±4.39 VS 117.28±6.55)、针刺伤防护知识认知评分(109.11±4.38 VS 96.44±6.72)、安全注射行为评分(38.45±4.91 VS 32.30±5.62)、职业认同感评分(58.02±8.55 VS 51.77±15.86)、岗位胜任力评分(82.59±13.35 VS 75.61±15.09)均高于常规组,差异均有统计学意义(均P<0.05)。干预组护理实习生锐器伤发生率(19.64%VS 57.14%)及平均发生频次(1.45 VS 2.13)均低于常规组;锐器伤后例次干预率(87.50%VS 45.59%)和例次上报率(93.75%VS 32.35%)均高于常规组,差异均有统计学意义(均P<0.05)。结论在护生岗前培训中引入项目式学习的锐器伤防护培训,能有效提升防护技能掌握程度,降低实习期间锐器伤发生率,对培养护生职业防护能力具有重要实践价值。
